Binary logistic regression
Definition taught in Logistic regression, part of Statistical learning
Binary logistic regression
| Type | Definition |
| Taught in | Logistic regression |
| Part | Statistical learning |
| Questions using it | 3 |
The Bernoulli likelihood and log-likelihood are
\begin{align*} L(\beta)&=\prod_{i=1}^n p_i^{y_i}(1-p_i)^{1-y_i},\\ \ell(\beta)&=\sum_{i=1}^n\bigl\{y_i\log p_i+(1-y_i)\log(1-p_i)\bigr\}, \end{align*}
with p_i=\sigma(\beta^\top x_i) and y_i\in\{0,1\}.
Used in
- Taught in Logistic regression, where the derivation and examples are.
- Also used in Classification trees · Ensembles, random forests, and boosting · Neural units · Networks in practice · High-dimensional analysis
- Questions that use it (3) — A logistic rule in an unfamiliar setting · Counting weights · A tempting wrong turn
- All 7 questions on Logistic regression
See also
Interpreting a logistic regression · Logit link · Odds ratio · Likelihood function · Iris data · Sigmoid function · Softmax is multinomial logistic regression · A finite stationary point is a global maximiser